bullet Sensors & Transducers Journal

    (ISSN 1726- 5479)


2008 e-Impact Factor

25 Top Downloaded Articles

Best Selling Articles 2012

Journal Subscription 2013

Editorial Calendar 2013

Submit an Article

Editorial Board

Current Issue

S&T journal's cover

Sensors & Transducers Journal 2011

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents


Best Articles 2011




Vol. 159, Issue 11, November 2013, pp. 32-38




Unsupervised Segmentation Method for Diseases of Soybean Color Image Based on Fuzzy Clustering
* Jiangsheng Gui, Li Hao, Shusen Sen, Wenshu Li, Yanfei Liu

College of Information, Zhejiang Sci-Tech University, Hangzhou 310018, China

* Tel: 086-571-86843320

* E-mail: dewgjs@126.com


Received: 28 August 2013   /Accepted: 25 October 2013   /Published: 30 November 2013

Digital Sensors and Sensor Sysstems


Abstract: The method of color image segmentation based on Fuzzy C-Means (FCM) clustering is simple, intuitive and is to be implemented. However, the clustering performance is affected by the center point of initialization and high computation and other issues. In this research, we propose a new color image unsupervised segmentation method based on fuzzy clustering. This method combines advantages of the fuzzy C-means algorithm and unsupervised clustering algorithm. Firstly, by gradually changing clusters c, and according to validity measurement, it can unsupervised search for optimal clusters c; then in order to achieve higher accuracy of clustering effect, the distance measurement scale was improved. In our experiments, this method was applied to color image segmentation for three kinds of soybean diseases. The results show that this method can more accurately segment the lesion area from the color image, and the segmentation processing of soybean disease is ideal, robustness, and have a high accuracy.


Keywords: Signal processing, FCM, Clustering, Diseases segmentation.


Acrobat reader logo Click <here> or title of paper to download the full pages article in pdf format



Download <here> the Library Journal Recommendation Form






1999 - 2018 Copyright , International Frequency Sensor Association (IFSA). All Rights Reserved.

Home - News - Links - Archives - Tools - Voltage-to-Frequency Converters - Standardization - Patents - Marketplace - Projects - Wish List - e-Shop - Sensor Jobs - Membership - Videos - Publishing - Site Map - Subscribe - Search

 Members Area -Sensors Portal -Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Submit Press Release - Submit White Paper - Testimonies - Twitter - Facebook - LinkedIn